When Circles Collide: Unpacking TPACK Instruction in an Eighth-Grade Social Studies Classroom

The technological pedagogical content knowledge (TPACK) conceptual framework, since its introduction in 2006, has provided researchers and teacher educators a theoretical means by which to understand the knowledge required to teach effectively with technology. However, a clear picture of what TPACK looks like in practice is still in its infancy. Using a case-study approach, this research examines the teaching practices of an eighth-grade social studies teacher, specifically how the teacher operationalizes TPACK within his classroom. Four examples of TPACK practices are given along with the teacher's rationale for his technology use. Two themes emerged: the transformation of technology into something that just happens in the classroom and the teacher's use of technology to shape instruction. Implications from this case study are then discussed, including the process and timing of combining the different knowledge components of TPACK and the need to better capture the role of students in the TPACK model.     

Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM

Mobile banking (mBanking) enables customers to carry out their banking tasks via mobile devices. We advance the extant body of knowledge about mBanking adoption by proposing a model for understanding the importance and relationship between the user perception of mBanking, initial trust in mBanking services, and the fit between the technology and mBanking task characteristics. We synergistically combine the strengths of three IS theories – task technology fit (TTF) model, unified theory of acceptance and usage of technology (UTAUT), and initial trust model (ITM). The model was tested in a study conducted in Portugal, one of the European Union (EU) countries with the highest mobile phone adoption. Based on the sample of 194 individuals we applied partial least squares (PLS) to test the conceptual model propose. The path significance levels were estimated using the bootstrapping method (500 resamples). The study found that facilitating conditions and behavioral intentions directly influence mBanking adoption. Initial trust, performance expectancy, technology characteristics, and task technology fit have total effect on behavioral intention. The paper offers valuable insights to decision-makers involved in the implementation and deployment of mBanking services. For researchers, the paper highlights the usefulness of integrating TTF, UTAUT and ITM in the development of a decision support framework to study the adoption of new technologies.     

A microfluidic device to study cancer metastasis under chronic and intermittent hypoxia

Metastatic cancer cells must traverse a microenvironment ranging from extremely hypoxic, within the tumor, to highly oxygenated, within the host''s vasculature. Tumor hypoxia can be further characterized by regions of both chronic and intermittent hypoxia. We present the design and characterization of a microfluidic device that can simultaneously mimic the oxygenation conditions observed within the tumor and model the cell migration and intravasation processes. This device can generate spatial oxygen gradients of chronic hypoxia and produce dynamically changing hypoxic microenvironments in long-term culture of cancer cells.     

Physiological determinants of speciality of elite middle- and long-distance runners

The aim of this study was to determine which physiological variables predict excellence in middle- and long-distance runners. Forty middle-distance runners (age 23 ± 4 years, body mass 67.2 ± 5.9 kg, stature 1.80 ± 0.05 m, VO(2max) 65.9 ± 4.5 ml · kg(-1) · min(-1)) and 32 long-distance runners (age 25 ± 4 years, body mass 59.8 ± 5.1 kg, stature 1.73 ± 0.06 m, VO(2max) 71.6 ± 5.0 ml · kg(-1) · min(-1)) competing at international standard performed an incremental running test to exhaustion. Expired gas analysis was performed breath-by-breath and maximum oxygen uptake (VO(2max)) and two ventilatory thresholds (VT(1) and VT(2)) were calculated. Long-distance runners presented a higher VO(2max) than middle-distance runners when expressed relative to body mass (P < 0.001, d = 1.18, 95% CI [0.68, 1.68]). At the intensities corresponding to VT(1) and VT(2), long-distance runners showed higher values for VO(2) expressed relative to body mass or %VO(2max), speed and oxygen cost of running (P < 0.05). When oxygen uptake was adjusted for body mass, differences between groups were consistent. Logistic binary regression analysis showed that VO(2max) (expressed as l · min(-1) and ml · kg(-1) · min(-1)), VO(2VT2) (expressed as ml · kg(-0.94) · min(-1)), and speed at VT(2) (v(VT2)) categorized long-distance runners. In addition, the multivariate model correctly classified 84.7% of the athletes. Thus, VO(2max), VO(2VT2), and v(VT2) discriminate between elite middle-distance and long-distance runners.     

Towards an econophysics view of intellectual capital dynamics: from self-organized criticality to the stochastic frontier

The paper begins with research studying the concept and nature of Intellectual Capital (IC), as well as how close IC firms are to the stochastic frontier. Then basic concepts of complexity theory – such as agents, self-organized criticality (SOC), connectivities, fractals, and power laws (PLs) – are used to distinguish between two kinds of IC firms’ success: traditional SOC applications to how firms maintain their position in a changing industry vs. how an IC firm (such as Apple) creates a new stochastic frontier. The research sets up PLs as indicators of whether or not firms and industries are SOC-effective and includes propositions about: (1) How IC firms benefit from complexity dynamics and SOC; (2) How PL distributions are indicators of efficacious SOC and adaptivity; and (3) Why IC attributes serve to create more transient dynamics pertaining to the stochastic frontier and the rest of the industry's rank/frequency distribution.     

Hierarchical Text Categorization Using Neural Networks

This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.     

Impact case studies: what accounts for the need for numbers in impact evaluation?

ABSTRACT

The UK Research Excellence Framework (REF) introduced impact as an indicator in the evaluation of higher education research quality in 2014. Impact case studies (ICS) are the basis of this evaluation. ICS use narrative explanations of how research causes ‘benefits to society’. This article analyses the evolving roles of the ICS and the relationships of ICS with various stakeholders. It draws on insights from legitimacy theory, Porter’s analysis of the social meaning of objectivity, and the insight from economist Alan Goodhart on the dysfunctional transformation of indicators into targets. It highlights the challenges to ICS evaluators and shows how a spiral of mistrust can both undermine but also cause demand for numbers. It explores whether impact narratives represent an escape from dominant research evaluation metrics and concludes that they do not.